Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification

© 2001-2011 IEEE. Accurate classification of Electroencephalogram (EEG) signals plays an important role in diagnoses of different type of mental activities. One of the most important challenges, associated with classification of EEG signals is how to design an efficient classifier consisting of strong generalization capability. Aiming to improve the classification performance, in this paper, we propose a novel multiclass support matrix machine (M-SMM) from the perspective of maximizing the inter-class margins. The objective function is a combination of binary hinge loss that works on C matrices and spectral elastic net penalty as regularization term. This regularization term is a combination of Frobenius and nuclear norm, which promotes structural sparsity and shares similar sparsity patterns across multiple predictors. It also maximizes the inter-class margin that helps to deal with complex high dimensional noisy data. The extensive experiment results supported by theoretical analysis and statistical tests show the effectiveness of the M-SMM for solving the problem of classifying EEG signals associated with motor imagery in brain-computer interface applications

Integrating joint feature selection into subspace learning: A formulation of 2DPCA for outliers robust feature selection

© 2019 Elsevier Ltd Since the principal component analysis and its variants are sensitive to outliers that affect their performance and applicability in real world, several variants have been proposed to improve the robustness. However, most of the existing methods are still sensitive to outliers and are unable to select useful features. To overcome the issue of sensitivity of PCA against outliers, in this paper, we introduce two-dimensional outliers-robust principal component analysis (ORPCA) by imposing the joint constraints on the objective function. ORPCA relaxes the orthogonal constraints and penalizes the regression coefficient, thus, it selects important features and ignores the same features that exist in other principal components. It is commonly known that square Frobenius norm is sensitive to outliers. To overcome this issue, we have devised an alternative way to derive objective function. Experimental results on four publicly available benchmark datasets show the effectiveness of joint feature selection and provide better performance as compared to state-of-the-art dimensionality-reduction methods

Progressive ShallowNet for large scale dynamic and spontaneous facial behaviour analysis in children

COVID-19 has severely disrupted every aspect of society and left negative impact on our life. Resisting the temptation in engaging face-to-face social connection is not as easy as we imagine. Breaking ties within social circle makes us lonely and isolated, that in turns increase the likelihood of depression related disease and even can leads to death by increasing the chance of heart disease. Not only adults, children's are equally impacted where the contribution of emotional competence to social competence has long term implications. Early identification skill for facial behaviour emotions, deficits, and expression may help to prevent the low social functioning. Deficits in young children's ability to differentiate human emotions can leads to social functioning impairment. However, the existing work focus on adult emotions recognition mostly and ignores emotion recognition in children. By considering the working of pyramidal cells in the cerebral cortex, in this paper, we present progressive lightweight shallow learning for the classification by efficiently utilizing the skip-connection for spontaneous facial behaviour recognition in children. Unlike earlier deep neural networks, we limit the alternative path for the gradient at the earlier part of the network by increase gradually with the depth of the network. Progressive ShallowNet is not only able to explore more feature space but also resolve the over-fitting issue for smaller data, due to limiting the residual path locally, making the network vulnerable to perturbations. We have conducted extensive experiments on benchmark facial behaviour analysis in children that showed significant performance gain comparatively

Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media

The ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words’ importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction

Enhanced Heartbeat Graph for emerging event detection on Twitter using time series networks

© 2019 Elsevier Ltd With increasing popularity of social media, Twitter has become one of the leading platforms to report events in real-time. Detecting events from Twitter stream requires complex techniques. Event-related trending topics consist of a group of words which successfully detect and identify events. Event detection techniques must be scalable and robust, so that they can deal with the huge volume and noise associated with social media. Existing event detection methods mostly rely on burstiness, mainly the frequency of words and their co-occurrences. However, burstiness sometimes dominates other relevant details in the data which could be equally significant. Besides, the topological and temporal relationships in the data are often ignored. In this work, we propose a novel graph-based approach, called the Enhanced Heartbeat Graph (EHG), which detects events efficiently. EHG suppresses dominating topics in the subsequent data stream, after their first detection. Experimental results on three real-world datasets (i.e., Football Association Challenge Cup Final, Super Tuesday, and the US Election 2012) show superior performance of the proposed approach in comparison to the state-of-the-art techniques

Robust Sparse Representation and Multiclass Support Matrix Machines for the Classification of Motor Imagery EEG Signals

© 2013 IEEE. Background: EEG signals are extremely complex in comparison to other biomedical signals, thus require an efficient feature selection as well as classification approach. Traditional feature extraction and classification methods require to reshape the data into vectors that results in losing the structural information exist in the original featured matrix. Aim: The aim of this work is to design an efficient approach for robust feature extraction and classification for the classification of EEG signals. Method: In order to extract robust feature matrix and reduce the dimensionality of from original epileptic EEG data, in this paper, we have applied robust joint sparse PCA (RJSPCA), Outliers Robust PCA (ORPCA) and compare their performance with different matrix base feature extraction methods, followed by classification through support matrix machine. The combination of joint sparse PCA with robust support matrix machine showed good generalization performance for classification of EEG data due to their convex optimization. Results: A comprehensive experimental study on the publicly available EEG datasets is carried out to validate the robustness of the proposed approach against outliers. Conclusion: The experiment results, supported by the theoretical analysis and statistical test, show the effectiveness of the proposed framework for solving classification of EEG signals

THE SIMULTANEOUS USE OF EM CONDUCTIVITY AND RADIOHM RESISTIVITY TECHNIQUES IN SOLUTION CHANNEL DETECTION

The chalky limestone and marley limestone are the main lithological unit of Euphrates Formation which can be existed and appeared in the area under investigation "north west Haditha area". When the groundwater passing through these types of rocks, a part of their may suffer from dissolution by means of dissolving process. Hence, the main task of the present survey is to locate and delineate the buried channels caused by this type of process in the area located in western part of Iraq by applying two EM techniques namely, electromagnetic conductivity and VLF-Radiohm electromagnetic resistivity methods. The Geonics EM34-3 together with the EM16R instruments were used in the field survey. More than ten predetermined traverses were pegged out in the area at which the EM measurements along these traverses were carried out. The results come from both surveys in correlation with available geological information have obviously showed a trend of clear and identity feature described as a single buried dissolved channel

What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter

© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter

Medical Students’ Perceptions of Peer Assessment in a Problem-based Learning Curriculum

Objectives: Peer assessment (PA) is believed to support learning and help students develop both professionally and personally. The aim of this study was to examine medical students’ perceptions of intragroup PA in a problem-based learning (PBL) setting. Methods: This study was carried out between September and November 2014 and involved six random groups of fourth-year undergraduate medical students (n = 60) enrolled at the Arabian Gulf University in Manama, Bahrain. While working on set tasks within a curriculum unit, each student evaluated a randomly selected peer using an English language adapted assessment tool to measure responsibility and respect, information processing, critical analysis, interaction and collaborative skills. At the end of the unit, students’ perceptions of PA were identified using a specifically-designed voluntary and anonymous selfadministered questionnaire in English. Results: A total of 55 students participated in the study (response rate: 92%). The majority of students reported that their learning (60%), attendance (67%), respect towards group members (70%) and participation in group discussions (71%) improved as a result of PA. Regarding problem analysis skills, most participants believed that PA improved their ability to analyse problems (65%), identify learning needs (64%), fulfil tasks related to the analysis of learning needs (72%) and share knowledge within their group (74%). Lastly, a large proportion of students reported that this form of assessment helped them develop their communication (71%) and self-assessment skills (73%), as well as collaborative abilities (75%). Conclusion: PA was well accepted by the students in this cohort and led to self-reported improvements in learning, skills, attitudes, engagement and other indicators of personal and professional development. PA was also perceived to have a positive impact on intragroup attitudes

Lipid A-Ara4N as an alternate pathway for (colistin) resistance in Klebsiella pneumonia isolates in Pakistan

Objectives: This study aimed to explore mechanism of colistin resistance amongst Klebsiella pneumoniae isolates through plasmid mediated mcr-1 gene in Pakistan. Carbapenem and Colistin resistant K. pneumoniae isolates (n = 34) stored at - 80 °C as part of the Aga Khan University Clinical Laboratory strain bank were randomly selected and subjected to mcr-1 gene PCR. To investigate mechanisms of resistance, other than plasmid mediated mcr-1 gene, whole genome sequencing was performed on 8 clinical isolates, including 6 with colistin resistance (MIC \u3e 4 μg/ml) and 2 with intermediate resistance to colistin (MIC \u3e 2 μg/ml).Results: RT-PCR conducted revealed absence of mcr-1 gene in all isolates tested. Whole genome sequencing results revealed modifications in Lipid A-Ara4N pathway. Modifications in Lipid A-Ara4N pathway were detected in ArnA_ DH/FT, UgdH, ArnC and ArnT genes. Mutation in ArnA_ DH/FT gene were detected in S3, S5, S6 and S7 isolates. UgdH gene modifications were found in all isolates except S3, mutations in ArnC were present in all except S1, S2 and S8 and ArnT were detected in all except S4 and S7. In the absence of known mutations linked with colistin resistance, lipid pathway modifications may possibly explain the phenotype resistance to colistin, but this needs further exploration